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- W3116130135 abstract "We present a method for adjacency matrix design by applying both discrete and continuous optimization techniques, which are powered by energy minimization and gradient descent respectively. Most of the subspace clustering methods design the adjacency matrix by adopting a common calculation rule over the entire data set, which is under the assumption that a same noise model is shared between points. Therefore, these methods tend to fail when the points contain multi-source noises. To relax this limitation, we introduce a method that finds the neighborhoods by considering discrete-continuous optimization. Our method outperforms competitive methods on both synthetic and real-world data." @default.
- W3116130135 created "2021-01-05" @default.
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- W3116130135 date "2020-10-13" @default.
- W3116130135 modified "2023-10-16" @default.
- W3116130135 title "Subspace-Dependent Adjacency Matrix Design via Discrete-Continuous optimization" @default.
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- W3116130135 doi "https://doi.org/10.1109/gcce50665.2020.9291831" @default.
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